Prioritizing Unknown Transformation Products from Biologically-Treated Wastewater Using High-Resolution Mass Spectrometry, Multivariate Statistics, and Metabolic Logic.

Abstract

Incomplete micropollutant elimination in wastewater treatment plants (WWTPs) results in transformation products (TPs) that are released into the environment. Improvements in analytical technologies have allowed researchers to identify several TPs from specific micropollutants but an overall picture of nontarget TPs is missing. In this study, we addressed this challenge by applying multivariate statistics to data collected with liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and subsequent tandem HRMS (MS/MS) in order to characterize peaks detected in the influent and effluent of a WWTP. Known biotransformation reactions were used to link potential parent compounds and TPs, while the structural similarity of these pairs hypothesized by MS/MS similarity was used for further prioritization. The methodology was validated with a set of spiked compounds, which included 25 parent/TP pairs for which analytical standards were available. This procedure was then applied to nontarget data, and 20 potential parent and TP pairs were selected for identification. In summary, primarily a surfactant homologue series, with associated TPs, was detected. Some obstacles still remain, including spectral interferences from coeluting compounds and identification of TPs, whose structures are less likely to be present in compound databases. The workflow was developed using openly accessible tools and, after parameter adjustment, could be applied to any data set with before and after information about various biological or chemical processes.